A likelihood ratio test for stationarity of rating transitions
AbstractFor a time-continuous discrete-state Markov process as model for rating transitions, we study the time-stationarity by means of a likelihood ratio test. For multiple Markov process data from a multiplicative intensity model, maximum likelihood parameter estimates can be represented as martingale transform of the processes counting transitions between the rating states. As a consequence, the profile partial likelihood ratio is asymptotically X-2-distributed. An internal rating data set reveals highly significant instationarity. --
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Bibliographic InfoPaper provided by Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen in its series Technical Reports with number 2008,27.
Date of creation: 2008
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Stationarity; Multiple Markov process; Counting process; Likelihood ratio; Panel data;
Find related papers by JEL classification:
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
- C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
- C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
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